Impact of Marker Pruning Strategies Based on Different Measurements of Marker Distance on Genomic Prediction in Dairy Cattle.

With the availability of high-density single-nucleotide polymorphism (SNP) data and the development of genotype imputation methods, high-density panel-based genomic prediction (GP) has become possible in livestock breeding. It is generally considered that the genomic estimated breeding value (GEBV) accuracy increases with the marker density, while studies have shown that the GEBV accuracy does not increase or even decrease when high-density panels were used. Therefore, in addition to the SNP number, other measurements of 'marker density' seem to have impacts on the GEBV accuracy, and exploring the relationship between the GEBV accuracy and the measurements of 'marker density' based on high-density SNP or whole-genome sequence data is important for the field of GP. In this study, we constructed different SNP panels with certain SNP numbers (e.g., 1 k) by using the physical distance (PhyD), genetic distance (GenD) and random distance (RanD) between SNPs respectively based on the high-density SNP data of a Germany Holstein dairy cattle population. Therefore, there are three different panels at a certain SNP number level. These panels were used to construct GP models to predict fat percentage, milk yield and somatic cell score. Meanwhile, the mean (d¯) and variance (σd2) of the physical distance between SNPs and the mean (r2¯) and variance (σr22) of the genetic distance between SNPs in each panel were used as marker density-related measurements and their influence on the GEBV accuracy was investigated. At the same SNP number level, the d¯ of all panels is basically the same, but the σd2, r2¯ and σr22 are different. Therefore, we only investigated the effects of σd2, r2¯ and σr22 on the GEBV accuracy. The results showed that at a certain SNP number level, the GEBV accuracy was negatively correlated with σd2, but not with r2¯ and σr22. Compared with GenD and RanD, the σd2 of panels constructed by PhyD is smaller. The low and moderate-density panels (< 50 k) constructed by RanD or GenD have large σd2, which is not conducive to genomic prediction. The GEBV accuracy of the low and moderate-density panels constructed by PhyD is 3.8~34.8% higher than that of the low and moderate-density panels constructed by RanD and GenD. Panels with 20-30 k SNPs constructed by PhyD can achieve the same or slightly higher GEBV accuracy than that of high-density SNP panels for all three traits. In summary, the smaller the variation degree of physical distance between adjacent SNPs, the higher the GEBV accuracy. The low and moderate-density panels construct by physical distance are beneficial to genomic prediction, while pruning high-density SNP data based on genetic distance is detrimental to genomic prediction. The results provide suggestions for the development of SNP panels and the research of genome prediction based on whole-genome sequence data.

Identification of key Genes and Pathways Associated With Thermal Stress in Peripheral Blood Mononuclear Cells of Holstein Dairy Cattle.

The objectives of the present study were to identify key genes and biological pathways associated with thermal stress in Chinese Holstein dairy cattle. Hence, we constructed a cell-model, applied various molecular biology experimental techniques and bioinformatics analysis. A total of 55 candidate genes were screened from published literature and the IPA database to examine its regulation under cold (25°C) or heat (42°C) stress in PBMCs. We identified 29 (3 up-regulated and 26 down-regulated) and 41 (15 up-regulated and 26 down-regulated) significantly differentially expressed genes (DEGs) (fold change ≥ 1.2-fold and P < 0.05) after cold and heat stress treatments, respectively. Furthermore, bioinformatics analyses confirmed that major biological processes and pathways associated with thermal stress include protein folding and refolding, protein phosphorylation, transcription factor binding, immune effector process, negative regulation of cell proliferation, autophagy, apoptosis, protein processing in endoplasmic reticulum, estrogen signaling pathway, pathways related to cancer, PI3K- Akt signaling pathway, and MAPK signaling pathway. Based on validation at the cellular and individual levels, the mRNA expression of the HIF1A gene showed upregulation during cold stress and the EIF2A, HSPA1A, HSP90AA1, and HSF1 genes showed downregulation after heat exposure. The RT-qPCR and western blot results revealed that the HIF1A after cold stress and the EIF2A, HSPA1A, HSP90AA1, and HSF1 after heat stress had consistent trend changes at the cellular transcription and translation levels, suggesting as key genes associated with thermal stress response in Holstein dairy cattle. The cellular model established in this study with PBMCs provides a suitable platform to improve our understanding of thermal stress in dairy cattle. Moreover, this study provides an opportunity to develop simultaneously both high-yielding and thermotolerant Chinese Holstein cattle through marker-assisted selection.

Quantitative assessment of German Holstein dairy cattle colostrum and impact of thermal treatment on quality of colostrum viscosity and immunoglobulins.

OBJECTIVE: This study aimed to determine the color, fat, viscosity, IgG concentration, %Brix and refractive index of fresh postpartum colostrum of German Holstein dairy cattle and assess the impact of different thermal treatments on the visual and dynamic viscosity, in association to IgG concentration, of colostrum that can be used for pasteurization process. RESULTS: Of the total 40 fresh postpartum colostrum, the color of colostrum (ranging from white-pale yellow to yellow and dark-yellowish), fat (1.4-8.2 100 g-1), IgG (4-116 mg mL-1), %Brix (8.5-35.4%), refractive index (1.3454-1.3905 nD), visual (ranging from watery to liquid and thick) and dynamic (4.9-219 cp) viscosity, were recorded. Statistical analysis between visual and dynamic viscosity of fresh colostrum showed significant correlation coefficients (rs = 634). Moreover, a significant correlation between viscosity and three IgG concentrations was also observed. Heat-treated colostrum showed dynamic viscosity ranged from 25 to 3066 cP, where dynamic viscosity of colostrum before- and after heat-treatment showed no significant correlation. Treated colostrum at 60 °C/60 min and 63.5 °C/30 min containing IgG concentration ≤ 80 mg mL-1 and ≤ 68 mg mL-1 showed no significant change in the viscosity and can successfully be applied for pasteurization of first postpartum colostrum.

Genome-wide association analysis and gene set enrichment analysis with SNP data identify genes associated with 305-day milk yield in Holstein dairy cows.

Milk production traits, such as 305-day milk yield (305MY), have been under direct selection to improve production in dairy cows. Over the past 50 years, the average milk yield has nearly doubled, and over 56% of the increase is attributable to genetic improvement. As such, additional improvements in milk yield are still possible as new loci are identified. The objectives of this study were to detect SNPs and gene sets associated with 305MY in order to identify new candidate genes contributing to variation in milk production. A population of 781 primiparous Holstein cows from six central Washington dairies with records of 305MY and energy corrected milk were used to perform a genome-wide association analysis (GWAA) using the Illumina BovineHD BeadChip (777 962 SNPs) to identify QTL associated with 305MY (P < 1.0 × 10-5 ). A gene set enrichment analysis with SNP data (GSEA-SNP) was performed to identify gene sets (normalized enrichment score > 3.0) and leading edge genes (LEGs) influencing 305MY. The GWAA identified three QTL comprising 34 SNPs and 30 positional candidate genes. In the GSEA-SNP, five gene sets with 58 unique and 24 shared LEGs contributed to 305MY. Identification of QTL and LEGs associated with 305MY can provide additional targets for genomic selection to continue to improve 305MY in dairy cattle.

Influenza D Virus in Animal Species in Guangdong Province, Southern China.

Molecular tests revealed influenza D viruses of D/OK lineage widely circulating in farmed animal species in Guangdong Province, southern China. In particular, we found high levels of influenza D virus infection in goats and pigs. We also detected viral RNA in serum specimens and feces of animals with certain severe diseases.

Application of random regression models for genetic analysis of 305-d milk yield over different lactations of Iranian Holsteins.

OBJECTIVE: During the last decade, genetic evaluation of dairy cows using longitudinal data (test day milk yield or 305- day milk yield) using random regression method has been officially adopted in several countries. The objectives of this study were to estimate covariance functions for genetic and permanent environmental effects and to obtain genetic parameters of 305-day milk yield over seven parities. METHODS: Data including 60,279 total 305-day milk yield of 17,309 Iranian Holstein dairy cows in 7 parities calved between 20 to 140 months between 2004 and 2011. Residual variances were modeled by homogeneous and step functions with 7 and 10 classes. RESULTS: The results showed that a third order polynomial for additive genetic and permanent environmental effects plus a step function with 10 classes for the residual variance was the most adequate and parsimonious model to describe the covariance structure of the data. Heritability estimates obtained by this model varied from 0.17 to 0.28. The performance of this model was better than repeatability model. Moreover, 10 classes of residual variance produce the more accurate result than 7 classes or homogeneous residual effect. CONCLUSION: A quadratic Legendre polynomial for additive genetic and permanent environmental effects with 10 step function residual classes are sufficient to produce a parsimonious model that explained the change in 305-day milk yield over consecutive parities of Iranian Holstein cows.

Random Regression Models Using Legendre Polynomials to Estimate Genetic Parameters for Test-day Milk Protein Yields in Iranian Holstein Dairy Cattle.

The objective of this study was to estimate the genetic parameters of milk protein yields in Iranian Holstein dairy cattle. A total of 1,112,082 test-day milk protein yield records of 167,269 first lactation Holstein cows, calved from 1990 to 2010, were analyzed. Estimates of the variance components, heritability, and genetic correlations for milk protein yields were obtained using a random regression test-day model. Milking times, herd, age of recording, year, and month of recording were included as fixed effects in the model. Additive genetic and permanent environmental random effects for the lactation curve were taken into account by applying orthogonal Legendre polynomials of the fourth order in the model. The lowest and highest additive genetic variances were estimated at the beginning and end of lactation, respectively. Permanent environmental variance was higher at both extremes. Residual variance was lowest at the middle of the lactation and contrarily, heritability increased during this period. Maximum heritability was found during the 12th lactation stage (0.213±0.007). Genetic, permanent, and phenotypic correlations among test-days decreased as the interval between consecutive test-days increased. A relatively large data set was used in this study; therefore, the estimated (co)variance components for random regression coefficients could be used for national genetic evaluation of dairy cattle in Iran.